Development and Application of Web-based Machine Learning Program for Automated Assessment Model Generation
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In order to enable customized learning using artificial intelligence, a scoring model that can evaluate students' responses is needed. Human-scored data and programming skills training artificial intelligence are required to generate scoring models. When teachers develop scoring models, it is very useful to secure many scoring models that can be used in schools. However, the biggest challenge for teachers to create scoring models is programming skills training artificial intelligence. Thus, in this study, we developed a web-based automatic assessment model generation program that can rapidly generate assessment models using graded descriptive responses using supervised learning, without programming. Web program development was created by dividing an analyzer, classifier, web development, and server for extracting features. By developing an assessment model with actual scored data using the developed program, it was determined that an assessment model is reliable. Using the developed program, teachers can create their assessments using scored data without prior programming experience. In addition, the developed program can be used to strengthen teachers' artificial intelligence capabilities.
Abstract
In order to enable customized learning using artificial intelligence, a scoring model that can evaluate students' responses is needed. Human-scored data and programming skills training artificial intelligence are required to generate scoring models. When teachers develop scoring models, it is very useful to secure many scoring models that can be used in schools. However, the biggest challenge for teachers to create scoring models is programming skills training artificial intelligence. Thus, in this study, we developed a web-based automatic assessment model generation program that can rapidly generate assessment models using graded descriptive responses using supervised learning, without programming. Web program development was created by dividing an analyzer, classifier, web development, and server for extracting features. By developing an assessment model with actual scored data using the developed program, it was determined that an assessment model is reliable. Using the developed program, teachers can create their assessments using scored data without prior programming experience. In addition, the developed program can be used to strengthen teachers' artificial intelligence capabilities.
참고문헌 (16)
- Ha, M., "Assessing Scientific Practices Using Machine Learning Methods: Development of Automated Computer Scoring Models for Written Evolutionary Explanations", Doctor, pp.2013
- Ha, M., "Darwin’s difficulties and students’ struggles with trait loss : Cognitivehistorical parallelisms in evolutionary explanation", Science & Education, 23, pp.1051~1074 (2014)
- 하민수, "학습 도구로서의 서술형 평가 그리고 인공지능의 활용: WA3I 프로젝트 사례", 현장과학교육, 13(3), pp.271~282 (2019), 한국현장과학교육학회[DOI]
- Kim, K. H., "Kim's Deep Learning Camp for Natural Language Processing" (2019), Hanbit media
- Kim, W. D., "Fundamental limitations and challenges of creative human resources development", STEPI Insight, 32, pp.1~20 (2009)
- Landis, J. R., "The measurement of observer agreement for categorical data", pp.159~174 (1977), Biometrics
- 이빛나, "피드백 효과에 대한 메타분석: 피드백, 학습과제 및 학습자 특성에 따른 차이", 교육평가연구, 31(3), pp.501~529 (2018), 한국교육평가학회[DOI]
- Maestrales, S., "Using machine learning to score multi-dimensional assessments of chemistry and physics", Journal of Science Education and Technology, 30, pp.239~254 (2021)
- Moharreri, K., "EvoGrader : An online formative assessment tool for automatically evaluating written evolutionary explanations", Evolution : Education and Outreach, 7, pp.1~14 (2014)
- Nehm, R. H., "Item feature effects in evolution assessment", Journal of Research in Science Teaching, 48, pp.237~256 (2011)
- Opfer, J. E., "Cognitive foundations for science assessment design : Knowing what students know about evolution", Journal of Research in Science Teaching, 49, pp.744~777 (2012)
- Shute, V. J., "Focus on formative feedback", Review of Educational Research, 78, pp.153~189 (2008)
- Wiliam, D., "Embedded Formative Assessment" (2011), Soluton Tree Press
- Zhai, X., "Applying machine learning in science assessment : A systematic review", Studies in Science Education, 56, pp.111~151 (2020)
- Assessing argumentation using machine learning and cognitive diagnostic modeling, "Zhai, X.", Research in Science Education, 1~20 (2022)
- Zhai, X., "A meta-analysis of machine learning-based science assessments : Factors impacting machine-human score agreements", Journal of Science Education and Technology, 30, pp.361~379 (2021)